sklearn.metrics.f1_score

sklearn.metrics.f1_score(y_true, y_pred, *, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn')

Compute the F1 score, also known as balanced F-score or F-measure.

The F1 score can be interpreted as a harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. The formula for the F1 score is:

F1 = 2 * TP / (2 * TP + FN + FP)

Where “TP” is the number of true positives, “FN” is the number of false negatives, and “FP” is the number of false positives. F1 is by default calculated as 0.0 when there are no true positives, false negatives, nor false positives.

Support beyond term:binary targets is achieved by treating multiclass and multilabel data as a collection of binary problems, one for each label. For the binary case, setting average='binary' will return F1 score for pos_label. If average is not 'binary', pos_label is ignored and F1 score for both classes are computed, then averaged or both returned (when average=None). Similarly, for multiclass and multilabel targets, F1 score for all labels are either returned or averaged depending on the average parameter. Use labels specify the set of labels to calculate F1 score for.

Read more in the User Guide.

Parameters:

y_true1d array-like, or label indicator array / sparse matrix

Ground truth (correct) target values.

y_pred1d array-like, or label indicator array / sparse matrix

Estimated targets as returned by a classifier.

labelsarray-like, default=None

The set of labels to include when average != 'binary', and their order if average is None. Labels present in the data can be excluded, for example in multiclass classification to exclude a “negative class”. Labels not present in the data can be included and will be “assigned” 0 samples. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order.

Changed in version 0.17: Parameter labels improved for multiclass problem.

pos_labelint, float, bool or str, default=1

The class to report if average='binary' and the data is binary, otherwise this parameter is ignored. For multiclass or multilabel targets, set labels=[pos_label] and average != 'binary' to report metrics for one label only.

average{‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’} or None, default=’binary’

This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:

'binary':

Only report results for the class specified by pos_label. This is applicable only if targets (y_{true,pred}) are binary.

'micro':

Calculate metrics globally by counting the total true positives, false negatives and false positives.

'macro':

Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.

'weighted':

Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an F-score that is not between precision and recall.

'samples':

Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from accuracy_score).

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

zero_division{“warn”, 0.0, 1.0, np.nan}, default=”warn”

Sets the value to return when there is a zero division, i.e. when all predictions and labels are negative.

Notes: - If set to “warn”, this acts like 0, but a warning is also raised. - If set to np.nan, such values will be excluded from the average.

New in version 1.3: np.nan option was added.

Returns:

f1_scorefloat or array of float, shape = [n_unique_labels]

F1 score of the positive class in binary classification or weighted average of the F1 scores of each class for the multiclass task.

See also

fbeta_score

Compute the F-beta score.

precision_recall_fscore_support

Compute the precision, recall, F-score, and support.

jaccard_score

Compute the Jaccard similarity coefficient score.

multilabel_confusion_matrix

Compute a confusion matrix for each class or sample.

Notes

When true positive + false positive + false negative == 0 (i.e. a class is completely absent from both y_true or y_pred), f-score is undefined. In such cases, by default f-score will be set to 0.0, and UndefinedMetricWarning will be raised. This behavior can be modified by setting the zero_division parameter.

References

[1]

Wikipedia entry for the F1-score.

Examples

>>> import numpy as np
>>> from sklearn.metrics import f1_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> f1_score(y_true, y_pred, average='macro')
0.26...
>>> f1_score(y_true, y_pred, average='micro')
0.33...
>>> f1_score(y_true, y_pred, average='weighted')
0.26...
>>> f1_score(y_true, y_pred, average=None)
array([0.8, 0. , 0. ])

>>> # binary classification
>>> y_true_empty = [0, 0, 0, 0, 0, 0]
>>> y_pred_empty = [0, 0, 0, 0, 0, 0]
>>> f1_score(y_true_empty, y_pred_empty)
0.0...
>>> f1_score(y_true_empty, y_pred_empty, zero_division=1.0)
1.0...
>>> f1_score(y_true_empty, y_pred_empty, zero_division=np.nan)
nan...

>>> # multilabel classification
>>> y_true = [[0, 0, 0], [1, 1, 1], [0, 1, 1]]
>>> y_pred = [[0, 0, 0], [1, 1, 1], [1, 1, 0]]
>>> f1_score(y_true, y_pred, average=None)
array([0.66666667, 1.        , 0.66666667])

Examples using sklearn.metrics.f1_score

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Probability Calibration curves

Precision-Recall

Precision-Recall

Semi-supervised Classification on a Text Dataset

Semi-supervised Classification on a Text Dataset